This is the third course of the Advanced Machine Learning on GCP specialization. In this course,
We will take a look at different strategies for building an image classifier using convolutional neural networks. We'll improve the model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting our data. We will also look at practical issues that arise, for example, when you don’t have enough data and how to incorporate the latest research findings into our models.
You will get hands-on practice building and optimizing your own image classification models on a variety of public datasets in the labs we’ll work on together.
Prerequisites: Basic SQL, familiarity with Python and TensorFlow

Prazos flexíveis

Nível avançado

Aprox. 10 horas para completar

Inglês

Legendas: Inglês

Programa - O que você aprenderá com este curso

Semana

1

Horas para completar

1 hora para concluir

Welcome to Image Understanding with TensorFlow on GCP

In this introductory module you will learn about the rapid growth in high-resolution image data available and the types of applications that it can be applied to. We’ll also cover image data as inputs to your model.
...

Linear and DNN Models

We’ll start with a brief introduction where we’ll cover the image dataset you will be using for part of this course. Then we’ll tackle an image classification problem with a linear model in TensorFlow. After that we’ll move onto tackling the same problem using a Deep Neural Network. Lastly, we’ll close with a discussion and application of dropout which is a regularization technique for neural networks to help prevent them from memorizing our training dataset.
...

Convolutional Neural Networks (CNNs)

This module will introduce Convolutional Neural Networks or CNNs for short, and get you started with implementing CNNs using TensorFlow. Since 2012, CNN based systems achieved unparalleled performance on tasks like image recognition and even at playing the ancient board game of Go against the top human champions....

Going Deeper Faster

In this module, you will learn how to train deeper, more accurate networks and do such training faster.You will learn about common problems that arise when training deeper networks, and how researchers have been able to address these issues....

Pre-built ML Models for Image Classification

Welcome to the last module of the Image Classification course. Now that you have build your own image classifiers using linear, DNN, and CNN models with TensorFlow, it’s time to experiment with pre-built image models. In most cases, you will want to try these before investing your time in developing custom TensorFlow code for a model....

Summary

In this final module, we will review the core concepts covered in this image classification course. You will recall creating classifiers with linear models, DNNs, DNNs with Dropout, Convolutional Neural Networks (CNNs), and lastly with pre-built models like the Cloud Vision API and AutoML Vision. ...

Instrutores

Sobre Google Cloud

We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success....

This 5-course specialization focuses on advanced machine learning topics using Google Cloud Platform where you will get hands-on experience optimizing, deploying, and scaling production ML models of various types in hands-on labs. This specialization picks up where “Machine Learning on GCP” left off and teaches you how to build scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text. It ends with a course on building recommendation systems. Topics introduced in earlier courses are referenced in later courses, so it is recommended that you take the courses in exactly this order....

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